docstr: Evaluate model.
  
  :return: evaluation result as a dictionary of {'metric name': metric value}
docstr_tok: ["Evaluate","model",".",":","return",":","evaluation","result","as","a","dictionary","of","{","'metric","name","'",":","metric","value","}"]
code: def evaluate(self, **kwargs):
      """
      Evaluate model.
  
      :return: evaluation result as a dictionary of {'metric name': metric value}
      """
      # Need to preprocess params as pytorch_ray_estimator does.
      pass
  
imports: from bigdl.orca.learn.ray_estimator import BaseEstimator
  from bigdl.orca.ray import OrcaRayContext
  from bigdl.orca.data.file import enable_multi_fs_save, enable_multi_fs_load
  import io
  import torch
  import ray
  from ray.exceptions import RayActorError
  from abc import abstractmethod, ABCMeta
  from bigdl.orca.learn.pytorch.utils import find_free_port, check_for_failure
  from bigdl.orca.learn.utils import get_driver_node_ip
  from bigdl.dllib.utils.log4Error import invalidInputError, logging
  from typing import Dict
label_desc: Use image node_id 4 for calling the BaseRayEstimator obj's underlying member method code with example usage: obj.evaluate() without return types